Supplementary material to Table 3 of the article
The role of PR 10
proteins and molecular components of moulds and yeast in atopic dermatitis
patients, J. Čelakovská, R. Vaňková, H. Skalská, J. Krejsek & C. Andrýs
Links to the interactive graph and tables:
Top 8rules by Fisher Exact test
Explanation in Brief:
The sensitization of specific IgE
components (molecular components of PR 10 proteins, mould and yeast), obtained
with ALEX2 Allergy Explorer, was compared at different levels of atopic
dermatitis (AD).
The sets of associated positive allergens reagents are
presented (semiquantitative levels of measurement IgE
> 0.3).
Significant sets of molecular
components associated in pairs and triplets were in the relation to the
severity of atopic dermatitis, the occurrence of asthma bronchial, and allergic
rhinitis.
We used the association
rules method with the interest to study the most frequent sets of
simultaneously positive allergen reagents (PR 10 proteins and molecular components of moulds and
yeast) observed in a patient in coincidence with allergic
dermatitis.
Packages
arules, arulesViz (Hahsler, 2017, 2021; Hahsler et al., 2005; Webb, 2007) and several related R packages were used to specify
sets of associated components, and to present complementary characteristics in
interactive graphs and tables.
The
interactive plot and tables present sets of associated components and complementary
characteristics of a set (rule).
Association Rules:
The
association rules X→Y, where X on the left-hand side (LHS) represents items (allergens, AB, or AR). Y
on the right-hand side (RHS) represents AD. The length of the set on
the LHS can vary from 1 to a specified limit. Setting the maximal length of
rule 5 detects sets up to 5 associated reagents at any LHS. In addition, a minimum
threshold of 0.1 (corresponds to frequency 10 in our data) was set up as
support (frequency) of the rules which satisfied the limits on length.
Support
of a rule decreases with the increasing length of the rule.
The
confidence of the rule X→Y corresponds to the conditional
probability P(Y/X) and
estimates the probability of AD presented on RHS knowing that corresponding subset
X of allergen reagents on LHS is observed. The maximum value of confidence
equals one.
Lift
(the measure of interestingness) equals support(X, Y)/(support(X) . support (Y)).
The value measures the deviation of the support of the rule from the support
expected under independence (under independence X, Y, lift = 1). Lift values >> 1 indicate stronger
associations. The measure is sensitive to small values in the denominator (when
support is close to 0).
Vizualizations
Static look (details available in the interactive plot) presents
rules with significant associations (the significance level p, Fisher’s Exact
Test).
Lift
and confidence estimate the predictive quality, support describes
the relative frequency of the rule in the data set (n = 100), and the order
means the number of components of the rule. Circle diameters are proportional
to the support of a rule.
The presented set
of 8 rules with a significance level p < 0.01 presents an overall false
discovery rate of 0.05 (Holm adjustment).
:
Figure
1: Static look at the details of the selected rule
in the interactive graph. Top 8rules by Fisher Exact test
Circle diameters are proportional to the support
(relative frequency based on total n = 100 patients). Characteristics of a rule
are visible after approaching the mouse pointer to the circle. The upper left
part allows the selection of a component of the plot.
Statistical analysis in more detail is described in
the article [1], Table 3 and Table 4.
Figure 2: Static look to
the interactive table with selection to show 10 entries.
Top 50rules by lift
Symbols ∆∇ allow to sort (in the interactive mode) rows in
ascending or descending order on the selected column. In this view, data is
sorted in descending order by confidence.
Link to the
interactive table Top
97 rules min support 10
References
1.
Čelakovská J., Vaňková R., Skalská H., Krejsek
J. & Andrýs C.: The role of PR 10
proteins and molecular components of moulds and yeast in atopic dermatitis
patients. Food and
Agricultural Immunology, 33(1), 780-798, https://www.tandfonline.com/doi/full/10.1080/09540105.2022.2130183
2.
Hahsler M
(2021). arulesViz: Visualizing Association Rules and
Frequent Itemsets. R package version
1.5-1, https://CRAN.R-project.org/package=arulesViz.
3.
Hahsler M
(2017). “arulesViz: Interactive Visualization of
Association Rules with R.” R
Journal, 9(2), 163–175. ISSN 2073-4859
4.
Hahsler, M., Grün, B., Hornik, K., & Buchta, C. (2005). Introduction to arules
– A computational environment for mining association rules and frequent item
sets. Journal of Statistical Software, 14(15), http://www.jstatsoft.org/. https://doi.org/10.18637/jss.v014.i15
5.
Webb, G. I. (2007). Discovering significant
patterns. Machine Learning, 68(1), 1–33.
https://doi.org/10.1007/s10994-007-5006-x.
https://www.tandfonline.com/doi/full/10.1080/09540105.2022.2130183
https://doi.org/10.1080/09540105.2022.2130183